GCSTI: A Single-Cell Pseudotemporal Trajectory Inference Method Based on Graph Compression

Wenhui Tu, Guang Ling, Feng Liu, Fuyan Hu, Xiangxiang Song

Research output: Contribution to journalArticlepeer-review

Abstract

The single-cell pseudotemporal trajectory inference is an important way to explore the process of developmental changes within a cell. Due to the uneven rate of cell growth, changes in gene expression depend less on the time of data collection and more on a cell's 'internal clock'. To overcome the challenges of gene analysis, and replicate biological developmental processes, several strategies have been put forth. However, due to the size of single-cell datasets, locating relevant signposts usually necessitate clustering analysis or a sizable amount of priori information. To this end, we propose a novel single-cell pseudotemporal trajectory inference technique: GCSTI method, which is based on graph compression and doesn't rely on a priori knowledge or clustering procedures, can handle the trajectory inference problem for a large network in a stable and efficient manner. Additionally, we simultaneously improve the pseudotime defining method currently employed in this study in order to obtain more trustworthy and beneficial outcomes for trajectory inference. Finally, we validate the efficacy and stability of the GCSTI method using datasets from human skeletal muscle myogenic cells and four simulated datasets.

Original languageEnglish
Pages (from-to)2945-2958
Number of pages14
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume20
Issue number5
DOIs
StatePublished - 1 Sep 2023

Keywords

  • Graph compression
  • pseudotemporal trajectory inference
  • pseudotime definition
  • single-cell data

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